使用考克斯比例危险回归和机器学习预测术后肺癌复发和生存率
Lucy Pu1, Rajeev Dhupar2, Xin Meng3
1Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, USA.
Cancers
|January 11, 2025
概括
在手术后预测肺癌复发是具有挑战性的. 这项研究使用手术前CT扫描和机器学习来识别图像生物标志物,改善个性化患者监测的风险预测.
科学领域:
- 放射学和瘤学 放射学和瘤学
- 医疗成像医学成像
- 人工智能在医学中的应用
背景情况:
- 手术切除是早期肺癌的标准,但复发率 (30-50%) 仍然很高.
- 准确预测复发的可能性和手术后的时间是一个重大的临床挑战.
- 来自手术前CT扫描的新型图像生物标志物被探索以预测复发.
研究的目的:
- 从术前胸部CT扫描中识别新的图像生物标志物,以预测术后肺癌复发.
- 提高在非小细胞肺癌 (NSCLC) 患者中复发风险和时间的预测.
- 为了实现个性化的监测策略,并尽量减少肺癌复发.
主要方法:
- 分析了309名接受肺切除的NSCLC患者的队列.
- 考克斯比例危险回归和机器学习 (ML) 方法用于确定复发风险因素.
- 预测性能使用接收器操作特征 (ROC) 曲线 (AUC) 下的面积来评估.
主要成果:
- 手术程序,TNM分期,淋巴结参与,身体组成和瘤特征显著预测了复发风险 (p < 0.05).
- 考克斯和ML模型都显示了可比的预测性能,AUC从0.75到0.77.
- 确定了影响局部/区域,远程复发,无复发存活率 (RFS) 和整体存活率 (OS) 的关键因素.
结论:
- 手术前的胸部CT扫描显示了预测术后肺癌复发和生存的可行性.
- 已识别的图像生物标志物和预测模型为改善肺癌管理提供了潜力.
- 临床整合需要在更大的多站点队列中进行进一步验证.
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